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Course Outline
Introduction
Understanding the Fundamentals of Artificial Intelligence and Machine Learning
Understanding Deep Learning
- Overview of the Basic Concepts of Deep Learning
- Differentiating Between Machine Learning and Deep Learning
- Overview of Applications for Deep Learning
Overview of Neural Networks
- What are Neural Networks
- Neural Networks vs Regression Models
- Understanding Mathematical Foundations and Learning Mechanisms
- Constructing an Artificial Neural Network
- Understanding Neural Nodes and Connections
- Working with Neurons, Layers, and Input and Output Data
- Understanding Single Layer Perceptrons
- Differences Between Supervised and Unsupervised Learning
- Learning Feedforward and Feedback Neural Networks
- Understanding Forward Propagation and Back Propagation
- Understanding Long Short-Term Memory (LSTM)
- Exploring Recurrent Neural Networks in Practice
- Exploring Convolutional Neural Networks in practice
- Improving the Way Neural Networks Learn
Overview of Deep Learning Techniques Used in Banking
- Neural Networks
- Natural Language Processing
- Image Recognition
- Speech Recognition
- Sentimental Analysis
Exploring Deep Learning Case Studies for Banking
- Anti-Money Laundering Programs
- Know-Your-Customer (KYC) Checks
- Sanctions List Monitoring
- Billing Fraud Oversight
- Risk Management
- Fraud Detection
- Product and Customer Segmentation
- Performance Evaluation
- General Compliance Functions
Understanding the Benefits of Deep Learning for Banking
Exploring the Different Deep Learning Libraries for Python
- TensorFlow
- Keras
Setting Up Python with the TensorFlow for Deep Learning
- Installing the TensorFlow Python API
- Testing the TensorFlow Installation
- Setting Up TensorFlow for Development
- Training Your First TensorFlow Neural Net Model
Setting Up Python with Keras for Deep Learning
Building Simple Deep Learning Models with Keras
- Creating a Keras Model
- Understanding Your Data
- Specifying Your Deep Learning Model
- Compiling Your Model
- Fitting Your Model
- Working with Your Classification Data
- Working with Classification Models
- Using Your Models
Working with TensorFlow for Deep Learning for Banking
- Preparing the Data
- Downloading the Data
- Preparing Training Data
- Preparing Test Data
- Scaling Inputs
- Using Placeholders and Variables
- Specifying the Network Architecture
- Using the Cost Function
- Using the Optimizer
- Using Initializers
- Fitting the Neural Network
- Building the Graph
- Inference
- Loss
- Training
- Training the Model
- The Graph
- The Session
- Train Loop
- Evaluating the Model
- Building the Eval Graph
- Evaluating with Eval Output
- Training Models at Scale
- Visualizing and Evaluating Models with TensorBoard
Hands-on: Building a Deep Learning Credit Risk Model Using Python
Extending your Company's Capabilities
- Developing Models in the Cloud
- Using GPUs to Accelerate Deep Learning
- Applying Deep Learning Neural Networks for Computer Vision, Voice Recognition, and Text Analysis
Summary and Conclusion
Requirements
- Experience with Python programming
- General familiarity with financial and banking concepts
- Basic familiarity with statistics and mathematical concepts
28 Hours
Delivery Options
Private Group Training
Our identity is rooted in delivering exactly what our clients need.
- Pre-course call with your trainer
- Customisation of the learning experience to achieve your goals -
- Bespoke outlines
- Practical hands-on exercises containing data / scenarios recognisable to the learners
- Training scheduled on a date of your choice
- Delivered online, onsite/classroom or hybrid by experts sharing real world experience
Private Group Prices RRP from €9120 online delivery, based on a group of 2 delegates, €2880 per additional delegate (excludes any certification / exam costs). We recommend a maximum group size of 12 for most learning events.
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Public Training
Please see our public courses
Testimonials (2)
Organization, adhering to the proposed agenda, the trainer's vast knowledge in this subject
Ali Kattan - TWPI
Course - Natural Language Processing with TensorFlow
Very updated approach or CPI (tensor flow, era, learn) to do machine learning.
